A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

Author:

Lan Yunshi1,He Gaole23,Jiang Jinhao4,Jiang Jing1,Zhao Wayne Xin34,Wen Ji-Rong234

Affiliation:

1. School of Computing and Information Systems, Singapore Management University

2. School of Information, Renmin University of China

3. Beijing Key Laboratory of Big Data Management and Analysis Methods

4. Gaoling School of Artificial Intelligence, Renmin University of China

Abstract

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA. We begin with introducing the background about the KBQA task. Next, we present the two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. We then review the advanced methods comprehensively from the perspective of the two categories. Specifically, we explicate their solutions to the typical challenges. Finally, we conclude and discuss some promising directions for future research.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 70 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GS-CBR-KBQA: Graph-structured case-based reasoning for knowledge base question answering;Expert Systems with Applications;2024-12

2. May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability;International Journal of Human–Computer Interaction;2024-08-08

3. Entity-Alignment Interaction Model Based on Chinese RoBERTa;Applied Sciences;2024-07-15

4. Let Me Show You Step by Step: An Interpretable Graph Routing Network for Knowledge-based Visual Question Answering;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

5. unKR: A Python Library for Uncertain Knowledge Graph Reasoning by Representation Learning;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3